红外热像仪发射率补偿模型的研究
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1.上海第二工业大学智能制造与控制工程学院;2.上海第二工业大学能源与材料学院

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Research on emissivity compensation model of infrared thermal imager
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1.School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University;2.School of Energy and Materials, Shanghai Second Polytechnic University

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    摘要:

    红外热像仪作为非接触式测温工具在热冲压工艺中具有显著优势,但其测量精度易受表面发射率、观测角度及目标温度等多因素影响。文中提出一种动态发射率补偿的测温优化方法:首先采用投影测量技术精准获取复杂曲面零件的空间角度参数,通过实验量化分析观测角度和温度值对测温偏差的作用规律;通过机器学习算法构建发射率与多维变量间的非线性映射模型,实现动态发射率参数的智能补偿。实验验证表明,经补偿后测温系统误差可稳定控制在±1.5℃范围内,较固定发射率模式精度提升达60%,为高精度红外测温在智能制造场景中的应用提供了有效解决方案。

    Abstract:

    Infrared thermal imagers, as non-contact temperature measurement tools, have significant advantages in hot stamping processes, but their measurement accuracy is easily affected by multiple factors such as surface emissivity, observation angle, and target temperature. A temperature measurement optimization method with dynamic emissivity compensation is proposed in the article: firstly, projection measurement technology is used to accurately obtain the spatial angle parameters of complex curved parts, and the effect of observation angle and temperature values on temperature measurement deviation is quantitatively analyzed through experiments; Constructing a nonlinear mapping model between emissivity and multidimensional variables through machine learning algorithms to achieve intelligent compensation of dynamic emissivity parameters. Experimental verification shows that after compensation, the temperature measurement system error can be stably controlled within the range of ± 1.5 ℃, with an accuracy improvement of up to 60% compared to the fixed emissivity mode. This provides an effective solution for the application of high-precision infrared temperature measurement in intelligent manufacturing scenarios.

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